178 research outputs found
Translating Phrases in Neural Machine Translation
Phrases play an important role in natural language understanding and machine
translation (Sag et al., 2002; Villavicencio et al., 2005). However, it is
difficult to integrate them into current neural machine translation (NMT) which
reads and generates sentences word by word. In this work, we propose a method
to translate phrases in NMT by integrating a phrase memory storing target
phrases from a phrase-based statistical machine translation (SMT) system into
the encoder-decoder architecture of NMT. At each decoding step, the phrase
memory is first re-written by the SMT model, which dynamically generates
relevant target phrases with contextual information provided by the NMT model.
Then the proposed model reads the phrase memory to make probability estimations
for all phrases in the phrase memory. If phrase generation is carried on, the
NMT decoder selects an appropriate phrase from the memory to perform phrase
translation and updates its decoding state by consuming the words in the
selected phrase. Otherwise, the NMT decoder generates a word from the
vocabulary as the general NMT decoder does. Experiment results on the Chinese
to English translation show that the proposed model achieves significant
improvements over the baseline on various test sets.Comment: Accepted by EMNLP 201
Learning Vertex Representations for Bipartite Networks
Recent years have witnessed a widespread increase of interest in network
representation learning (NRL). By far most research efforts have focused on NRL
for homogeneous networks like social networks where vertices are of the same
type, or heterogeneous networks like knowledge graphs where vertices (and/or
edges) are of different types. There has been relatively little research
dedicated to NRL for bipartite networks. Arguably, generic network embedding
methods like node2vec and LINE can also be applied to learn vertex embeddings
for bipartite networks by ignoring the vertex type information. However, these
methods are suboptimal in doing so, since real-world bipartite networks concern
the relationship between two types of entities, which usually exhibit different
properties and patterns from other types of network data. For example,
E-Commerce recommender systems need to capture the collaborative filtering
patterns between customers and products, and search engines need to consider
the matching signals between queries and webpages
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